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https://github.com/datawhalechina/llms-from-scratch-cn.git
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Delete appendix-A/03_main-chapter-code/code-part2.ipynb
This commit is contained in:
Beyondzjl
GitHub
parent
e788c49ea0
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "O9i6kzBsZVaZ"
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},
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"source": [
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"# Appendix A: Introduction to PyTorch (Part 2)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "ppbG5d-NZezH"
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},
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"source": [
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"## A.9 Optimizing training performance with GPUs"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "6jH0J_DPZhbn"
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},
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"source": [
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"### A.9.1 PyTorch computations on GPU devices"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "RM7kGhwMF_nO",
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"outputId": "ac60b048-b81f-4bb0-90fa-1ca474f04e9a"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2.0.1+cu118\n"
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]
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}
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],
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"source": [
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"import torch\n",
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"\n",
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"print(torch.__version__)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "OXLCKXhiUkZt",
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"outputId": "39fe5366-287e-47eb-cc34-3508d616c4f9"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"True\n"
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]
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}
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],
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"source": [
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"print(torch.cuda.is_available())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "MTTlfh53Va-T",
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"outputId": "f31d8bbe-577f-4db4-9939-02e66b9f96d1"
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([5., 7., 9.])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tensor_1 = torch.tensor([1., 2., 3.])\n",
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"tensor_2 = torch.tensor([4., 5., 6.])\n",
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"\n",
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"print(tensor_1 + tensor_2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "Z4LwTNw7Vmmb",
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"outputId": "1c025c6a-e3ed-4c7c-f5fd-86c14607036e"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tensor([5., 7., 9.], device='cuda:0')\n"
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]
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}
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],
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"source": [
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"tensor_1 = tensor_1.to(\"cuda\")\n",
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"tensor_2 = tensor_2.to(\"cuda\")\n",
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"\n",
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"print(tensor_1 + tensor_2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 184
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},
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"id": "tKT6URN1Vuft",
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"outputId": "e6f01e7f-d9cf-44cb-cc6d-46fc7907d5c0"
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},
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"outputs": [
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{
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"ename": "RuntimeError",
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"evalue": "ignored",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-7-4ff3c4d20fc3>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mtensor_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtensor_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor_1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtensor_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;31mRuntimeError\u001b[0m: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!"
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]
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}
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],
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"source": [
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"tensor_1 = tensor_1.to(\"cpu\")\n",
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"print(tensor_1 + tensor_2)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "c8j1cWDcWAMf"
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},
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"source": [
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"## A.9.2 Single-GPU training"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"id": "GyY59cjieitv"
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},
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"outputs": [],
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"source": [
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"X_train = torch.tensor([\n",
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" [-1.2, 3.1],\n",
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" [-0.9, 2.9],\n",
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" [-0.5, 2.6],\n",
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" [2.3, -1.1],\n",
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" [2.7, -1.5]\n",
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"])\n",
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"\n",
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"y_train = torch.tensor([0, 0, 0, 1, 1])\n",
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"\n",
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"X_test = torch.tensor([\n",
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" [-0.8, 2.8],\n",
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" [2.6, -1.6],\n",
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"])\n",
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"\n",
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"y_test = torch.tensor([0, 1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"id": "v41gKqEJempa"
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},
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"outputs": [],
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"source": [
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"from torch.utils.data import Dataset\n",
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"\n",
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"\n",
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"class ToyDataset(Dataset):\n",
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" def __init__(self, X, y):\n",
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" self.features = X\n",
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" self.labels = y\n",
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"\n",
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" def __getitem__(self, index):\n",
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" one_x = self.features[index]\n",
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" one_y = self.labels[index]\n",
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" return one_x, one_y\n",
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"\n",
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" def __len__(self):\n",
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" return self.labels.shape[0]\n",
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"\n",
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"train_ds = ToyDataset(X_train, y_train)\n",
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"test_ds = ToyDataset(X_test, y_test)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {
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"id": "UPGVRuylep8Y"
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},
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"outputs": [],
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"source": [
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"from torch.utils.data import DataLoader\n",
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"\n",
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"torch.manual_seed(123)\n",
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"\n",
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"train_loader = DataLoader(\n",
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" dataset=train_ds,\n",
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" batch_size=2,\n",
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" shuffle=True,\n",
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" num_workers=1,\n",
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" drop_last=True\n",
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")\n",
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"\n",
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"test_loader = DataLoader(\n",
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" dataset=test_ds,\n",
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" batch_size=2,\n",
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" shuffle=False,\n",
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" num_workers=1\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {
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"id": "drhg6IXofAXh"
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},
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"outputs": [],
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"source": [
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"class NeuralNetwork(torch.nn.Module):\n",
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" def __init__(self, num_inputs, num_outputs):\n",
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" super().__init__()\n",
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"\n",
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" self.layers = torch.nn.Sequential(\n",
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"\n",
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" # 1st hidden layer\n",
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" torch.nn.Linear(num_inputs, 30),\n",
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" torch.nn.ReLU(),\n",
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"\n",
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" # 2nd hidden layer\n",
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" torch.nn.Linear(30, 20),\n",
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" torch.nn.ReLU(),\n",
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"\n",
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" # output layer\n",
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" torch.nn.Linear(20, num_outputs),\n",
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" )\n",
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"\n",
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" def forward(self, x):\n",
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" logits = self.layers(x)\n",
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" return logits"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "7jaS5sqPWCY0",
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"outputId": "84c74615-38f2-48b8-eeda-b5912fed1d3a"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch: 001/003 | Batch 000/002 | Train/Val Loss: 0.75\n",
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"Epoch: 001/003 | Batch 001/002 | Train/Val Loss: 0.65\n",
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"Epoch: 002/003 | Batch 000/002 | Train/Val Loss: 0.44\n",
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"Epoch: 002/003 | Batch 001/002 | Train/Val Loss: 0.13\n",
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"Epoch: 003/003 | Batch 000/002 | Train/Val Loss: 0.03\n",
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"Epoch: 003/003 | Batch 001/002 | Train/Val Loss: 0.00\n"
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]
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}
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],
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"source": [
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"import torch.nn.functional as F\n",
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"\n",
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"\n",
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"torch.manual_seed(123)\n",
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"model = NeuralNetwork(num_inputs=2, num_outputs=2)\n",
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"\n",
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"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") # NEW\n",
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"model = model.to(device) # NEW\n",
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"\n",
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"optimizer = torch.optim.SGD(model.parameters(), lr=0.5)\n",
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"\n",
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"num_epochs = 3\n",
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"\n",
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"for epoch in range(num_epochs):\n",
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"\n",
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" model.train()\n",
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" for batch_idx, (features, labels) in enumerate(train_loader):\n",
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"\n",
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" features, labels = features.to(device), labels.to(device) # NEW\n",
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" logits = model(features)\n",
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" loss = F.cross_entropy(logits, labels) # Loss function\n",
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"\n",
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" optimizer.zero_grad()\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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"\n",
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" ### LOGGING\n",
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" print(f\"Epoch: {epoch+1:03d}/{num_epochs:03d}\"\n",
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" f\" | Batch {batch_idx:03d}/{len(train_loader):03d}\"\n",
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" f\" | Train/Val Loss: {loss:.2f}\")\n",
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"\n",
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" model.eval()\n",
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" # Optional model evaluation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {
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"id": "4qrlmnPPe7FO"
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},
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"outputs": [],
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"source": [
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"def compute_accuracy(model, dataloader, device):\n",
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"\n",
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" model = model.eval()\n",
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" correct = 0.0\n",
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" total_examples = 0\n",
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"\n",
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" for idx, (features, labels) in enumerate(dataloader):\n",
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"\n",
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" features, labels = features.to(device), labels.to(device) # New\n",
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"\n",
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" with torch.no_grad():\n",
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" logits = model(features)\n",
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"\n",
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" predictions = torch.argmax(logits, dim=1)\n",
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" compare = labels == predictions\n",
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" correct += torch.sum(compare)\n",
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" total_examples += len(compare)\n",
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"\n",
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" return (correct / total_examples).item()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "1_-BfkfEf4HX",
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"outputId": "473bf21d-5880-4de3-fc8a-051d75315b94"
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1.0"
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]
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},
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"compute_accuracy(model, train_loader, device=device)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "iYtXKBGEgKss",
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"outputId": "508edd84-3fb7-4d04-cb23-9df0c3d24170"
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1.0"
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]
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},
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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"compute_accuracy(model, test_loader, device=device)"
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]
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"gpuType": "T4",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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